{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-3,3,50)\n",
    "y = 2*x-1\n",
    "y2 = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD1CAYAAACrz7WZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1af92cc8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x,y, label=\"linear line\")\n",
    "plt.plot(x,y2,color=\"red\",linewidth=1,linestyle=\"--\", label=\"square label\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.xlim((-1,2))\n",
    "plt.ylim((-2,3))\n",
    "x_tick = np.linspace(-1,2,5)\n",
    "plt.xticks(x_tick)\n",
    "plt.yticks([-2,-1.8,-1,1.22,3],[\"$really\\ bad$\",\"$bad$\",\"$normal$\",\"$good$\",\"$really good$\"])\n",
    "ax = plt.gca()\n",
    "ax.spines['right'].set_color('red')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data', 0))\n",
    "ax.spines['bottom'].set_color('blue')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1af92c354a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-3,3,50)\n",
    "y = 2*x+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
